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Volume 11
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Int. J. Financial Stud., Volume 11, Issue 3 (September 2023) – 34 articles

Cover Story (view full-size image): Amidst increased interest in machine learning (ML), multiple branches are seeking appropriate structures to develop, deploy, and maintain intelligent systems. For the finance industry, ML-based systems offer the potential to detect and monitor financial threats in an automated manner when making financial credit decisions. Since corporate credit ratings provide multiple strategic, financial, and managerial benefits for decision makers, it is necessary to test if ML-based internal models can be used to evaluate the financial situation of companies based on financial statements. Furthermore, the financial industry is highly regulated, so companies must deal with both technical and legal challenges when it comes to forecasting credit ratings. View this paper
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25 pages, 412 KiB  
Article
The Impact of Artificial Intelligence Disclosure on Financial Performance
by Fadi Shehab Shiyyab, Abdallah Bader Alzoubi, Qais Mohammad Obidat and Hashem Alshurafat
Int. J. Financial Stud. 2023, 11(3), 115; https://doi.org/10.3390/ijfs11030115 - 14 Sep 2023
Viewed by 9899
Abstract
This study determines to what extent Jordanian banks refer to and use artificial intelligence (AI) technologies in their operation process and examines the impact of AI-related terms disclosure on financial performance. Content analysis is used to analyze the spread of AI and related [...] Read more.
This study determines to what extent Jordanian banks refer to and use artificial intelligence (AI) technologies in their operation process and examines the impact of AI-related terms disclosure on financial performance. Content analysis is used to analyze the spread of AI and related information in the annual report textual data. Based on content analysis and regression analysis of data from 115 annual reports for 15 Jordanian banks listed in the Amman Stock Exchange for the period 2014 to 2021, the study reveals a consistent increase in the mention of AI-related terms disclosure since 2014. However, the level of AI-related disclosure remains weak for some banks, suggesting that Jordanian banks are still in the early stages of adopting and implementing AI technologies. The results indicate that AI-related keywords disclosure has an influence on banks’ financial performance. AI has a positive effect on accounting performance in terms of ROA and ROE and a negative impact on total expenses, which supports the dominant view that AI improves revenue and reduces cost and is also consistent with past literature findings. This study contributes to the growing body of AI literature, specifically the literature on AI voluntary disclosure, in several aspects. First, it provides an objective measure of the uses of AI by formulating an AI disclosure index that captures the status of AI adoption in practice. Second, it provides insights into the relationship between AI disclosure and financial performance. Third, it supports policymakers’, international authorities’, and supervisory organizations’ efforts to address AI disclosure issues and highlights the need for disclosure guidance requirements. Finally, it provides a contribution to banking sector practitioners who are transforming their operations using AI mechanisms and supports the need for more AI disclosure and informed decision making in a manner that aligns with the objectives of financial institutions. Full article
(This article belongs to the Topic Artificial Intelligence Applications in Financial Technology)
19 pages, 854 KiB  
Article
Green Electronic Auditing and Accounting Information Reliability in the Jordanian Social Security Corporation: The Mediating Role of Cloud Computing
by Ali Mahmoud Alrabei
Int. J. Financial Stud. 2023, 11(3), 114; https://doi.org/10.3390/ijfs11030114 - 13 Sep 2023
Cited by 2 | Viewed by 1487
Abstract
The purpose of this research is to examine the impact of green electronic auditing on accounting information reliability and the mediating role of cloud computing in the Jordanian Social Security Corporation. A survey of 500 employees in the Jordanian Social Security Corporation was [...] Read more.
The purpose of this research is to examine the impact of green electronic auditing on accounting information reliability and the mediating role of cloud computing in the Jordanian Social Security Corporation. A survey of 500 employees in the Jordanian Social Security Corporation was used to gather data, with a response rate of 31.4% (157 employees). The researcher used structural equation modeling to investigate the connections between cloud computing, auditing on data processing processes, auditing the inputs, auditing the outputs, prior auditing on inputs, and accounting information reliability. The findings revealed that auditing data processing activities, auditing outputs, cloud computing, and earlier auditing on inputs all have a substantial impact on accounting information reliability. However, auditing the inputs and the link between cloud computing and accounting information reliability were not significant. This study’s conclusions have ramifications for policymakers and auditing and accounting practitioners. The Jordanian Social Security Corporation must consider the significance of adequate auditing methods to assure correct accounting information, particularly in the context of cloud computing. This report also highlights the need for more research on the influence of cloud computing on accounting and auditing processes in underdeveloped countries. Full article
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30 pages, 2225 KiB  
Article
The Dynamic Return and Volatility Spillovers among Size-Based Stock Portfolios in the Saudi Market and Their Portfolio Management Implications during Different Crises
by Nassar S. Al-Nassar
Int. J. Financial Stud. 2023, 11(3), 113; https://doi.org/10.3390/ijfs11030113 - 12 Sep 2023
Viewed by 1349
Abstract
This study contributes to the ongoing debate on the size effect and size-based investment styles by investigating the return and volatility spillovers and time-varying conditional correlations among Saudi large-, mid-, and small-cap indices. To this end, we utilize the weekly returns on the [...] Read more.
This study contributes to the ongoing debate on the size effect and size-based investment styles by investigating the return and volatility spillovers and time-varying conditional correlations among Saudi large-, mid-, and small-cap indices. To this end, we utilize the weekly returns on the MSCI Saudi large-, mid-, and small-cap indices over a long sample period, spanning several crises. The econometric approach that we use is a VAR-asymmetric BEKK-GARCH model which accounts for structural breaks. On the basis of the VAR-asymmetric BEKK-GARCH model estimation results, we calculate portfolio weights and hedge ratios, and discuss their risk management implications. The empirical results confirm the presence of unilateral return spillovers running from mid- to small-cap stocks, while multilateral volatility spillovers are documented, albeit substantially weakened when accounting for structural breaks. The time-varying conditional correlations display clear spikes around crises, which translate to higher hedge ratios, increasing the cost of hedging during turbulent times. The optimal portfolio weights suggest that investors generally overweight large caps in their portfolios during uncertain times to minimize risk without lowering expected returns. The main takeaway from our results is that passively confining fund managers to a particular size category regardless of the prevailing market conditions may lead to suboptimal performance. Full article
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21 pages, 5441 KiB  
Article
Unveiling Market Connectedness: Dynamic Returns Spillovers in Asian Emerging Stock Markets
by Maaz Khan, Mrestyal Khan, Umar Nawaz Kayani, Khurrum Shahzad Mughal and Roohi Mumtaz
Int. J. Financial Stud. 2023, 11(3), 112; https://doi.org/10.3390/ijfs11030112 - 12 Sep 2023
Cited by 6 | Viewed by 1548
Abstract
This study investigates the returns spillovers across the equity markets of Asian emerging economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, South Korea, Taiwan, and Thailand). To achieve this objective, we used two different spillover methodologies (DY 2012 and BK 2018). Moreover, this study [...] Read more.
This study investigates the returns spillovers across the equity markets of Asian emerging economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, South Korea, Taiwan, and Thailand). To achieve this objective, we used two different spillover methodologies (DY 2012 and BK 2018). Moreover, this study used the daily closing prices of equity indices ranging from 5 January 2005 to 13 November 2021. The empirical findings revealed that the total spillover index using DY 2012, and the short-term frequency index using BK 2018, are close to each other, with values of 46.92% and 43.04%, respectively. However, the spillover index value is high, with a value of 56.25% in the long run. Furthermore, the results showed that the stock markets of South Korea and Taiwan are the major spillover transmitters in the Asian emerging markets. Also, the financial association among all emerging Asian equities is at its peak, subject to the mobility of cash flows across the global economies. The results of this study provide meaningful insight for policymakers and investors to implement an effective strategy to overcome the possible influence of any financial crisis in the future. Our paper provides a potential contribution to the financial literature by examining the transmission of spillovers across the Asian emerging stock markets. Furthermore, it provides in-depth information regarding stock market interdependence. Full article
(This article belongs to the Special Issue Macroeconomic and Financial Markets)
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27 pages, 3275 KiB  
Article
Market Reaction to Corporate Releases and News Articles: Evidence from Thailand’s Stock Market
by Likittanawong Supawat and Leemakdej Arnat
Int. J. Financial Stud. 2023, 11(3), 111; https://doi.org/10.3390/ijfs11030111 - 06 Sep 2023
Viewed by 1842
Abstract
Studies that quantify the price impact of the information in corporate press releases and news articles mainly focus on quantitative news, such as earnings announcements, dividends, and financial performance-related events, but leave out other corporate news events. Those that do so generally focus [...] Read more.
Studies that quantify the price impact of the information in corporate press releases and news articles mainly focus on quantitative news, such as earnings announcements, dividends, and financial performance-related events, but leave out other corporate news events. Those that do so generally focus on one source of information and do not compare the price impacts from different information sources. Our study aimed to extend previous studies by quantifying and comparing market reactions to corporate press releases and news articles across different news categories. We classified and categorized 100,960 news items, encompassing 26,546 corporate press releases and 74,414 news articles, of 615 firms in the Stock Exchange of Thailand between 1 January 2017 and 31 December 2019. These news items were classified into categories based on the information contained in corporate press releases and news articles. We then studied the market reactions to these news categories. We found that the price impact from corporate releases is sustained for both positive and negative news categories. Our results also show that the positive price impact for news reported by the media tends to reverse, consistent with prior studies. In contrast, the negative price impact from news articles holds; this result differs from previous studies. Our data also show that managers tend to leak and recycle good news while the release of bad news is delayed. Full article
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17 pages, 674 KiB  
Article
Enhancing Financial Fraud Detection through Addressing Class Imbalance Using Hybrid SMOTE-GAN Techniques
by Patience Chew Yee Cheah, Yue Yang and Boon Giin Lee
Int. J. Financial Stud. 2023, 11(3), 110; https://doi.org/10.3390/ijfs11030110 - 05 Sep 2023
Cited by 3 | Viewed by 2264
Abstract
The class imbalance problem in finance fraud datasets often leads to biased prediction towards the nonfraud class, resulting in poor performance in the fraud class. This study explores the effects of utilizing the Synthetic Minority Oversampling TEchnique (SMOTE), a Generative Adversarial Network (GAN), [...] Read more.
The class imbalance problem in finance fraud datasets often leads to biased prediction towards the nonfraud class, resulting in poor performance in the fraud class. This study explores the effects of utilizing the Synthetic Minority Oversampling TEchnique (SMOTE), a Generative Adversarial Network (GAN), and their combinations to address the class imbalance issue. Their effectiveness was evaluated using a Feed-forward Neural Network (FNN), Convolutional Neural Network (CNN), and their hybrid (FNN+CNN). This study found that regardless of the data generation techniques applied, the classifier’s hyperparameters can affect classification performance. The comparisons of various data generation techniques demonstrated the effectiveness of the hybrid SMOTE and GAN, including SMOTified-GAN, SMOTE+GAN, and GANified-SMOTE, compared with SMOTE and GAN. The SMOTified-GAN and the proposed GANified-SMOTE were able to perform equally well across different amounts of generated fraud samples. Full article
(This article belongs to the Topic Artificial Intelligence Applications in Financial Technology)
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27 pages, 5293 KiB  
Article
Effects of Contract Governance on the Relation of Partnership Critical Success Factors and the Performance of Malaysia Public-Private Partnership Initiatives
by Azlan Shah Abdul Latif, Noor Azman Ali, Zahira Ishan, Nor Siah Jaharuddin, Rohail Hassan and Adibah Abdul Latif
Int. J. Financial Stud. 2023, 11(3), 109; https://doi.org/10.3390/ijfs11030109 - 04 Sep 2023
Cited by 1 | Viewed by 1757
Abstract
Much research has been carried out to discover partnership critical success factors that influence public-private partnership success. Since most public-private partnership projects are long-term in nature and include contractual arrangements, there is still a lot to learn about contract governance’s role in public-private [...] Read more.
Much research has been carried out to discover partnership critical success factors that influence public-private partnership success. Since most public-private partnership projects are long-term in nature and include contractual arrangements, there is still a lot to learn about contract governance’s role in public-private partnership performance. Therefore, this study examines the effect of contract governance on the relationship between partnership critical success factors and partnership performance in Malaysia. Stakeholder Theory serves as the underpinning theory for this study. This study employed a quantitative method based on the positivist paradigm to distribute questionnaires. The information was collected from 261 contracting parties’ officials in Malaysian public-private partnership projects regulated by the Malaysian Public-Private Partnership Unit, and a stratified random sampling method was employed. The structural equation model analysis found that eight out of ten hypotheses were supported. According to this study, it has been established that contract governance has a direct favorable influence on partnership performance. However, it is also found that contract governance does not moderate the relationship between partnership critical success factors and partnership performance. Due to time constraints and the emergence of the COVID-19 pandemic, this study was from a cross-sectional viewpoint and adopted a quantitative methodology. The findings of this study are important in the contract governance and partnership performance literature, providing policymakers and concessionaires with new information on the impact of contract governance on public-private partnership project performance. Managers of public-private partnership projects should also be able to enhance their projects’ performance by understanding how contract governance influences the performance of their projects. Full article
(This article belongs to the Special Issue Cross-Cultural Corporate Governance, Firm Performance and Firm Value)
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14 pages, 278 KiB  
Review
A Review of the Implementation of Financial Technology (Fintech) in the Indonesian Agricultural Sector: Issues, Access, and Challenges
by Fathi Rufaidah, Tuti Karyani, Eliana Wulandari and Iwan Setiawan
Int. J. Financial Stud. 2023, 11(3), 108; https://doi.org/10.3390/ijfs11030108 - 04 Sep 2023
Cited by 3 | Viewed by 2852
Abstract
Technological developments, especially in the financial sector, are slowly changing the financial industry through digitalization towards fintech. The application of fintech has been introduced to Indonesia in the last few years; however, the existence and development of fintech in Indonesia still needs to [...] Read more.
Technological developments, especially in the financial sector, are slowly changing the financial industry through digitalization towards fintech. The application of fintech has been introduced to Indonesia in the last few years; however, the existence and development of fintech in Indonesia still needs to be studied further. This review provided a comprehensive overview of farmer technology accessibility, fintech preferences, fintech application impacts, fintech application problems, and challenges in the future. The review data are taken from the primary and secondary data related to fintech from numerous publications in Google Scholar, the interview of Indonesian farmers, and Indonesian Government data, including the Central Statistics Agency. This study confirmed that a fintech provider has been developed in Indonesia. The farmers’ accessibility to fintech was different between urban and rural areas due to the farmers’ education levels and the availability of the infrastructure. Fintech can provide practicality, ease of access, comfort, and cost-effectiveness, and can solve existing problems in Indonesian society. However, several problems arise, including infrastructure and internet access that is less supportive, as well as a lack of education, competent workers, and regulation. Agricultural fintech is a promising business in the future, despite the many challenges that need to be overcome for a stronger agricultural sector. Full article
(This article belongs to the Special Issue Literature Reviews in Finance)
16 pages, 314 KiB  
Article
Hidden Ownership and Firm Performance: Evidence from Thailand’s Initial Public Offering Firms
by Natthawut Wangwan and Arnat Leemakdej
Int. J. Financial Stud. 2023, 11(3), 107; https://doi.org/10.3390/ijfs11030107 - 04 Sep 2023
Viewed by 1536
Abstract
Previous studies have overlooked hidden ownership in their analysis, which could result in biased findings. This research utilizes unique data sources to uncover hidden ownership patterns and employs ordinary least square regression to investigate the relationship between hidden ownership and firm performance. The [...] Read more.
Previous studies have overlooked hidden ownership in their analysis, which could result in biased findings. This research utilizes unique data sources to uncover hidden ownership patterns and employs ordinary least square regression to investigate the relationship between hidden ownership and firm performance. The findings indicate that hidden ownership affects a firm’s performance, but not in the same manner as previously thought. Firms with hidden ownership actually perform better than those without. These results contradict the belief that hidden ownership leads to wealth expropriation from minority shareholders and negatively impacts a firm’s performance. The study also remains robust after accounting for potential endogeneity using an instrumental variable approach. The findings provide policy implications and contribute to the ownership and firm performance literatures. Full article
(This article belongs to the Special Issue Cross-Cultural Corporate Governance, Firm Performance and Firm Value)
16 pages, 4628 KiB  
Review
A Systematic Bibliometric Analysis of the Real Estate Bubble Phenomenon: A Comprehensive Review of the Literature from 2007 to 2022
by José-Francisco Vergara-Perucich
Int. J. Financial Stud. 2023, 11(3), 106; https://doi.org/10.3390/ijfs11030106 - 23 Aug 2023
Viewed by 1453
Abstract
This article presents the results of a bibliometric review of the study of real estate bubbles in the scientific literature indexed in Web of Science and Scopus, from 2007 to 2022. The analysis was developed using a sample of 2276 documents, which were [...] Read more.
This article presents the results of a bibliometric review of the study of real estate bubbles in the scientific literature indexed in Web of Science and Scopus, from 2007 to 2022. The analysis was developed using a sample of 2276 documents, which were reviewed in R software and analyzed with the assistance of the Bibliometrix package of the same software. The results indicate that there has been considerable productivity on the topic of real estate bubbles since 2007, with an emphasis on housing price formation processes and the social effects when bubbles burst. The authors found that there were not many case studies located in Latin America or Africa, nor were there approaches with advanced predictive modeling techniques using machine learning or artificial intelligence. The article provides an understanding of the state of the art in real estate bubble research and situates new research in front of the influential literature previously published. Full article
(This article belongs to the Special Issue Literature Reviews in Finance)
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20 pages, 2462 KiB  
Article
What Influenced Hanoi’s Apartment Price Bubble between 2010 and 2021?
by Phuong Lan Le, Anh Tuan Do and Anh Ngoc Pham
Int. J. Financial Stud. 2023, 11(3), 105; https://doi.org/10.3390/ijfs11030105 - 17 Aug 2023
Viewed by 1335
Abstract
This study focused on testing the existence of an apartment price bubble in Hanoi (Vietnam) and on determining the factors that affected it in the period between 2010 and 2021. Using the fundamental factor approach, the authors applied VAR regression using time series [...] Read more.
This study focused on testing the existence of an apartment price bubble in Hanoi (Vietnam) and on determining the factors that affected it in the period between 2010 and 2021. Using the fundamental factor approach, the authors applied VAR regression using time series data. Specifically, we used the ADF unit test to test the stationarity of the variables based on the following criteria: AIC (Akaike information criterion); LR (likelihood ratio); FPE (final prediction error); HQ (Hanan–Quinn information criterion); and Schwarz (SC) to find the optimal lag (Lag) for the model. We also applied the Granger causality test to determine the correlation between the economic variables that appeared in the model with the PR index. We present the results of the research model through the push–response function and the variance decomposition to consider and evaluate the impact of the PR index shock on itself and the other variables. The literature in this field includes many studies that are similar to this one; however, no research has been conducted that has focused on analysing whether variables, such as per capita income and the urbanisation rate, influence the formation of real estate bubbles. This focus is especially relevant in Hanoi, which is an important part of the Vietnamese real estate market. Through this study, we aimed to fill this gap and to contribute to the references on the Hanoi real estate market and its influencing factors. Full article
(This article belongs to the Special Issue Asset Pricing, Investments and Portfolio Management)
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14 pages, 1982 KiB  
Review
Bibliometric Review of Participatory Budgeting: Current Status and Future Research Agenda
by Miloš Milosavljević, Željko Spasenić and Jovan Krivokapić
Int. J. Financial Stud. 2023, 11(3), 104; https://doi.org/10.3390/ijfs11030104 - 17 Aug 2023
Cited by 1 | Viewed by 1711
Abstract
Participatory budgeting has been advocated as an advanced tool of civic participation and a travelling innovation for more than three decades. This paper provides a bibliometric review of the concurrent body of knowledge on participatory budgeting (PB), explaining how this democratic innovation ‘travelled’ [...] Read more.
Participatory budgeting has been advocated as an advanced tool of civic participation and a travelling innovation for more than three decades. This paper provides a bibliometric review of the concurrent body of knowledge on participatory budgeting (PB), explaining how this democratic innovation ‘travelled’ through time and over different scientific fields. This study was based on a dataset of 396 papers on PB published from 1989 to January 2023. The study finds that research in PB has reached its peak of scholarly attention in pre-COVID-19 pandemic years. The study also finds that the research on PB has migrated from the field of political science to other fields, such as economics, management science, law, urban planning, environmental science, and technology. Full article
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21 pages, 3918 KiB  
Review
Bibliometric Review of Blended Finance and Partial Risk Guarantee: Establishing Needs and Advantages
by Kamakshi Sharma, Tusharika Mahna, Sonali Jain, Sanjay Dhir, Neeta Rao, Achin Biyani, Himanshu Sikka, Rishit Yadav, Sidharth Dua and Archish Gupta
Int. J. Financial Stud. 2023, 11(3), 103; https://doi.org/10.3390/ijfs11030103 - 11 Aug 2023
Viewed by 1375
Abstract
A partial risk guarantee (PRG) is one of the critical instruments in the blended finance approach that provides partial assurance to the risk investor to lend leveraged capital to the borrower. Under the PRG scheme, philanthropic capital is employed as a risk guarantee [...] Read more.
A partial risk guarantee (PRG) is one of the critical instruments in the blended finance approach that provides partial assurance to the risk investor to lend leveraged capital to the borrower. Under the PRG scheme, philanthropic capital is employed as a risk guarantee to create financial and economic additionality through the multiplier effect. This study examines the current trends in PRG and blended finance ecosystem research. This study also aims to identify future research areas to work upon. The bibliometric analysis highlights the need and advantages of blended finance and PRG. The study highlights themes, such as climate finance, SDGs, impact investments, and blended finance/PRGs, from the literature on blended finance. This study illustrates the impact for researchers and managers regarding the future direction to undertake and the domains where PRG can work wonders. The research allows for a comprehensive view of the leading trends, such as utilising blended finance tools such as PRG in funding the work in climate financing, SDGs, water, sanitation, and impact investment. This is perhaps the first study to conduct a bibliometric analysis of the developing area of blended finance partial risk guarantee literature to highlight its importance and advantages. Full article
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16 pages, 308 KiB  
Article
Nexus of Financing Constraints and Supply Chain Finance: Evidence from Listed SMEs in China
by Sin-Huei Ng, Yunze Yang, Chin-Chong Lee and Chui-Zi Ong
Int. J. Financial Stud. 2023, 11(3), 102; https://doi.org/10.3390/ijfs11030102 - 10 Aug 2023
Cited by 2 | Viewed by 1593
Abstract
As opposed to developed markets, financing constraints are a more pressing issue among Small and Medium-Sized Enterprises (SMEs) in emerging markets. We explore the severity of financing constraints on SMEs, and examine the role of supply chain finance (SCF) in alleviating those constraints, [...] Read more.
As opposed to developed markets, financing constraints are a more pressing issue among Small and Medium-Sized Enterprises (SMEs) in emerging markets. We explore the severity of financing constraints on SMEs, and examine the role of supply chain finance (SCF) in alleviating those constraints, with the focus on a large emerging market: China. Using the panel data of SMEs listed on Shenzhen Stock Exchange from 2014 to 2020, we employ robust estimations of panel-corrected standard errors (PCSEs) and robust fixed-effects methods to analyze the issue. Our cash–cash-flow sensitivity model points out that listed SMEs in China show significant cash–cash-flow sensitivity, and financing constraints are prevalent. We document that the development of SCF has a mitigation effect on the financing constraints on the SMEs. Our robustness test with Yohai’s MM-estimator is also supportive of the main finding. Our study indicates the importance of supply chain finance development in alleviating the financing constraints on SMEs and, subsequently, supporting their sustainability journey. Overall, our findings have important policy implications for the stakeholders involved in emerging markets, and there are lessons to be learned from the Chinese experience. There is still much to be explored in the nexus of SCF and the financing difficulties of SMEs in China at present, with much of the extant literature concentrating only on specific financing mechanisms. Thus, our study fills the gap by providing a broad and comprehensive analysis of the issue. Full article
16 pages, 2151 KiB  
Article
Sentiments Extracted from News and Stock Market Reactions in Vietnam
by Loan Thi Vu, Dong Ngoc Pham, Hang Thu Kieu and Thuy Thi Thanh Pham
Int. J. Financial Stud. 2023, 11(3), 101; https://doi.org/10.3390/ijfs11030101 - 07 Aug 2023
Viewed by 2895
Abstract
News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover news sentiments and classify news according to its sentiments with the application of PhoBERT, [...] Read more.
News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover news sentiments and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors are concerned with the daily news by testing the movements of the market before and after the news is released. The results of the analysis show that there is an insignificant difference in the stock price as a response to the news. However, negative news sentiments can alter the variance of market returns. Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
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25 pages, 1284 KiB  
Article
Uncovering the Effect of News Signals on Daily Stock Market Performance: An Econometric Analysis
by Shahid Raza, Sun Baiqing, Pwint Kay-Khine and Muhammad Ali Kemal
Int. J. Financial Stud. 2023, 11(3), 99; https://doi.org/10.3390/ijfs11030099 - 04 Aug 2023
Cited by 3 | Viewed by 4720
Abstract
The stock markets in developing countries are highly responsive to breaking news and events. Our research explores the impact of economic conditions, financial policies, and politics on the KSE-100 index through daily market news signals. Utilizing simple OLS regression and ARCH/GARCH regression methods, [...] Read more.
The stock markets in developing countries are highly responsive to breaking news and events. Our research explores the impact of economic conditions, financial policies, and politics on the KSE-100 index through daily market news signals. Utilizing simple OLS regression and ARCH/GARCH regression methods, we determine the best model for analysis. The results reveal that political and global news has a significant impact on KSE-100 index. Blue chip stocks are considered safer investments, while short-term panic responses often overshadow rational decision-making in the stock market. Investors tend to quickly react to negative news, making them risk-averse. Our findings suggest that the ARCH/GARCH models are better at predicting stock market fluctuations compared to the simple OLS method. Full article
(This article belongs to the Special Issue Macroeconomic and Financial Markets)
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15 pages, 2070 KiB  
Article
The Changing Landscape of Financial Credit Risk Models
by Tanja Verster and Erika Fourie
Int. J. Financial Stud. 2023, 11(3), 98; https://doi.org/10.3390/ijfs11030098 - 04 Aug 2023
Viewed by 1989
Abstract
The landscape of financial credit risk models is changing rapidly. This study takes a brief look into the future of predictive modelling by considering some factors that influence financial credit risk modelling. The first factor is machine learning. As machine learning expands, it [...] Read more.
The landscape of financial credit risk models is changing rapidly. This study takes a brief look into the future of predictive modelling by considering some factors that influence financial credit risk modelling. The first factor is machine learning. As machine learning expands, it becomes necessary to understand how these techniques work and how they can be applied. The second factor is financial crises. Where predictive models view the future as a reflection of the past, financial crises can violate this assumption. This creates a new field of research on how to adjust predictive models to incorporate forward-looking conditions, which include future expected financial crises. The third factor considers the impact of financial technology (Fintech) on the future of predictive modelling. Fintech creates new applications for predictive modelling and therefore broadens the possibilities in the financial predictive modelling field. This changing landscape causes some challenges but also creates a wealth of opportunities. One way of exploiting these opportunities and managing the associated risks is via industry collaboration. Academics should join hands with industry to create industry-focused training and industry-focused research. In summary, this study made three novel contributions to the field of financial credit risk models. Firstly, it conducts an investigation and provides a comprehensive discussion on three factors that contribute to rapid changes in the credit risk predictive models’ landscape. Secondly, it presents a unique discussion of the challenges and opportunities arising from these factors. Lastly, it proposes an innovative solution, specifically collaboration between academic and industry partners, to effectively manage the challenges and take advantage of the opportunities for mutual benefits. Full article
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20 pages, 359 KiB  
Article
The Effect of Capital Structure on Firm Value: A Study of Companies Listed on the Vietnamese Stock Market
by Thi Ngoc Bui, Xuan Hung Nguyen and Kieu Trang Pham
Int. J. Financial Stud. 2023, 11(3), 100; https://doi.org/10.3390/ijfs11030100 - 04 Aug 2023
Cited by 4 | Viewed by 10885
Abstract
This research investigates the relationship between capital structure and firm value for companies listed on the Vietnamese stock market. The study utilizes data from audited financial statements of 769 companies spanning from 2012 to 2022, amounting to 8459 observations. Employing various estimation methods, [...] Read more.
This research investigates the relationship between capital structure and firm value for companies listed on the Vietnamese stock market. The study utilizes data from audited financial statements of 769 companies spanning from 2012 to 2022, amounting to 8459 observations. Employing various estimation methods, such as ordinary least squares (OLS), fixed effects model (FEM), random effects model (REM), and generalized least squares (GLS), the impact of capital structure on key financial indicators, namely, return on assets (ROA), return on equity (ROE), and Tobin’s Q, is assessed. The findings indicate that the debt ratio exhibits a positive influence on ROA, ROE, and Tobin’s Q, with Tobin’s Q displaying the most pronounced impact (0.450) and ROA showing the weakest impact (0.011). However, the long-term debt ratio does not significantly affect firm value. Interestingly, both short-term and long-term debt ratios have negative effects on ROA, ROE, and Tobin’s Q, with the most substantial impact on Tobin’s Q reduction (0.562). Based on these research outcomes, the authors offer valuable recommendations to companies, investors, business leaders, and policymakers to make informed decisions in selecting an optimal and sensible capital structure. Full article
17 pages, 372 KiB  
Article
Impact of Liquidity and Investors Sentiment on Herd Behavior in Cryptocurrency Market
by Siniša Bogdan, Natali Brmalj and Elvis Mujačević
Int. J. Financial Stud. 2023, 11(3), 97; https://doi.org/10.3390/ijfs11030097 - 31 Jul 2023
Viewed by 2264
Abstract
This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of [...] Read more.
This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of cryptocurrencies. This study aims to fill this research gap by examining the impact of liquidity and sentiment on herd behavior using the CSAD model, considering small, medium, and large cryptocurrencies. The results show different outcomes for cryptocurrencies of different sizes, consistently demonstrating that the herding effect is more pronounced under conditions of lower liquidity, as determined by the turnover volume and liquidity ratio of cryptocurrencies. Proxy measures such as the Twitter Hedonometer and CBOE VIX were used to measure investor sentiment and show the prevalence of herding behavior in optimistic times for all cryptocurrencies, regardless of their market capitalization. Consequently, this study provides valuable insights into the manifestation of herd behavior in the cryptocurrency market and highlights the importance of liquidity and sentiment as influencing factors. These findings improve our understanding of investor behavior and provide guidance to market participants and policymakers on how to effectively manage the risks associated with herd effects. Full article
20 pages, 2215 KiB  
Article
Opening a New Era with Machine Learning in Financial Services? Forecasting Corporate Credit Ratings Based on Annual Financial Statements
by Mustafa Pamuk and Matthias Schumann
Int. J. Financial Stud. 2023, 11(3), 96; https://doi.org/10.3390/ijfs11030096 - 30 Jul 2023
Viewed by 1639
Abstract
Corporate credit ratings provide multiple strategic, financial, and managerial benefits for decision-makers. Therefore, it is essential to have accurate and up-to-date ratings to continuously monitor companies’ financial situations when making financial credit decisions. Machine learning (ML)-based internal models can be used for the [...] Read more.
Corporate credit ratings provide multiple strategic, financial, and managerial benefits for decision-makers. Therefore, it is essential to have accurate and up-to-date ratings to continuously monitor companies’ financial situations when making financial credit decisions. Machine learning (ML)-based internal models can be used for the assessment of companies’ financial situations using annual statements. Particularly, it is necessary to check whether these ML models achieve better results compared to statistical methods. Due to the multi-class classification problem when forecasting corporate credit ratings, the development, monitoring, and maintenance of ML-based systems are more challenging compared to simple classifications. This problem becomes even more complex due to the required coordination with financial regulators (e.g., OECD, EBA, BaFin, etc.). Furthermore, the ML models must be updated regularly due to the periodic nature of annual statements as a dataset. To address the problem of the limited dataset, multiple sampling strategies and machine learning algorithms can be combined for accurate and up-to-date forecasting of credit ratings. This paper provides various implications for ML-based forecasting of credit ratings and presents an approach for combining sampling strategies and ML techniques. It also provides design recommendations for ML-based services in the finance industry on how to fulfill the existing regulations. Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
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16 pages, 311 KiB  
Article
Do Share Repurchases Crowd Out Internal Investment in South Africa?
by Gretha Steenkamp and Nicolene Wesson
Int. J. Financial Stud. 2023, 11(3), 95; https://doi.org/10.3390/ijfs11030095 - 27 Jul 2023
Viewed by 1084
Abstract
Researchers in developed countries have questioned whether share repurchase activity influences internal investment. The aim of this study was to investigate the relationship between share repurchases and internal investment (defined as capital expenditure, employment expenditure, and research and development) in South Africa, as [...] Read more.
Researchers in developed countries have questioned whether share repurchase activity influences internal investment. The aim of this study was to investigate the relationship between share repurchases and internal investment (defined as capital expenditure, employment expenditure, and research and development) in South Africa, as little was known about this relationship in developing countries. A quantitative research methodology was followed, employing the data of South African listed companies during the 2002–2017 period. A significant negative relationship was noted between share repurchases and employment expenditure when considering all companies, while high-growth companies exhibited a significant negative relationship between share repurchases and capital expenditure. The negative relationships could indicate that companies increase share repurchases to the detriment of internal investment (especially employment). Alternatively, it may imply that share repurchase and internal investment decisions are determined simultaneously, with companies decreasing internal investment and increasing share repurchases in the absence of identifiable profitable projects (or increasing internal investment and decreasing share repurchases when growth opportunities are available). These findings could be useful to shareholders, corporate governance regulators and activists. Given the high unemployment and income inequality in South Africa, the results support a call for the improved regulation of share repurchases to ensure effective monitoring. Full article
22 pages, 5280 KiB  
Review
Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications
by Gaurang Sonkavde, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy and Subraya Krishna Bhat
Int. J. Financial Stud. 2023, 11(3), 94; https://doi.org/10.3390/ijfs11030094 - 26 Jul 2023
Cited by 12 | Viewed by 28574
Abstract
The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of [...] Read more.
The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models. Full article
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25 pages, 3463 KiB  
Review
The Sustainability of Investing in Cryptocurrencies: A Bibliometric Analysis of Research Trends
by Mohammad Alqudah, Luis Ferruz, Emilio Martín, Hanan Qudah and Firas Hamdan
Int. J. Financial Stud. 2023, 11(3), 93; https://doi.org/10.3390/ijfs11030093 - 25 Jul 2023
Cited by 8 | Viewed by 3780
Abstract
This paper explores the state of the art in the cryptocurrency literature, with a special emphasis on the links between financial dimensions and ESG features. The study uses bibliometric analysis to illustrate the history of cryptocurrency publication activity, focusing on the most popular [...] Read more.
This paper explores the state of the art in the cryptocurrency literature, with a special emphasis on the links between financial dimensions and ESG features. The study uses bibliometric analysis to illustrate the history of cryptocurrency publication activity, focusing on the most popular subjects and research trends. Between 2014 and 2021, 1442 papers on cryptocurrencies were published in the Web of Science core collection, the most authoritative database, although only a tiny percentage evaluated ESG factors. One of the most common criticisms of cryptocurrencies is the pollution derived from energy consumption in their mining process and their use for illicit purposes due to the absence of effective regulation. The study allows us to suggest future research directions that may be beneficial in illustrating the environmental effect, studying financial behavior, identifying the long-term sustainability of cryptocurrencies, and evaluating their financial success. This study provides an in-depth examination of current research trends in the field of cryptocurrencies, identifying prospective future research directions. Full article
(This article belongs to the Special Issue Digital and Conventional Assets)
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22 pages, 841 KiB  
Review
Board Compensation in Financial Sectors: A Systematic Review of Twenty-Four Years of Research
by Saleh F. A. Khatib, Hamzeh Al Amosh and Husam Ananzeh
Int. J. Financial Stud. 2023, 11(3), 92; https://doi.org/10.3390/ijfs11030092 - 24 Jul 2023
Cited by 1 | Viewed by 1894
Abstract
We aim to provide a comprehensive systematic analysis of scholarly publications in the field of board compensation in financial sectors extending through the years 1987 to 2021. Hence, the most notable themes, theories, and contributions to the literature are identified, and research developments [...] Read more.
We aim to provide a comprehensive systematic analysis of scholarly publications in the field of board compensation in financial sectors extending through the years 1987 to 2021. Hence, the most notable themes, theories, and contributions to the literature are identified, and research developments over time are evaluated. With the identification of a final sample of 87 research papers indexed in Scopus, we identify research gaps to provide insight into future research following a systematic method. The results revealed that the United States of America received the broadest research interest, along with cross-country research. While the literature lacked to provide investigations for other countries of the world. Although the effect of compensation on organizational outcomes (performance and grow) is still unclear in the literature, several factors have been introduced as key drivers of the compensation, including the country’s level of development, the development of equity markets, the development of banking system, its dependence on foreign capital, collective rights empowering labor, the strength of a country’s welfare institutions, employment market forces, and social order and authority relations. On a theoretical level, agency theory has been most popular in the literature, along with providing multiple theoretical frameworks with agency theory as a slack resources theory, managerial talent theory, and managerial power theory. This is the first research to our knowledge that used a systematic review (SR) of literature to give a complete and comprehensive evaluation of the literature on board compensation in the financial sector. The current study documents the flow of literature on the board’s compensation in the financial sectors over 24 years and establishes future research opportunities. Full article
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19 pages, 268 KiB  
Article
The Market’s View on Accounting Classifications for Asset Securitizations
by Minkwan Ahn
Int. J. Financial Stud. 2023, 11(3), 91; https://doi.org/10.3390/ijfs11030091 - 11 Jul 2023
Viewed by 1077
Abstract
Prior research has examined how investors view asset securitizations, and shows that investors treat securitizations as borrowings, even when GAAP treats them as sales. Upon the adoption of two new accounting standards relating to securitizations, some off-balance-sheet securitized assets were consolidated back onto [...] Read more.
Prior research has examined how investors view asset securitizations, and shows that investors treat securitizations as borrowings, even when GAAP treats them as sales. Upon the adoption of two new accounting standards relating to securitizations, some off-balance-sheet securitized assets were consolidated back onto firms’ balance sheets. This study investigated how investors viewed assets that firms consolidated under the new standards and those that firms left unconsolidated. I found that investors differentiated between these two types of securitizations, treating the consolidated assets as borrowings and the unconsolidated assets as sales. I conclude that the new accounting standards are more consistent with equity investors’ views of securitizations. I also found that, for the consolidated assets, investors did not distinguish between securitizations going through two different accounting structures. Lastly, this study provides evidence on one information channel that investors use to distinguish between securitizations that may have the economic substance of borrowings versus sales. Full article
18 pages, 441 KiB  
Article
Building Trust in Fintech: An Analysis of Ethical and Privacy Considerations in the Intersection of Big Data, AI, and Customer Trust
by Hassan H. H. Aldboush and Marah Ferdous
Int. J. Financial Stud. 2023, 11(3), 90; https://doi.org/10.3390/ijfs11030090 - 10 Jul 2023
Cited by 6 | Viewed by 18780
Abstract
This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature-review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership, [...] Read more.
This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature-review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership, and control. The findings emphasize the importance of safeguarding customer data, complying with data protection laws, and promoting corporate digital responsibility. The study provides practical suggestions for companies, including the use of encryption techniques, transparency regarding data collection and usage, the provision of customer opt-out options, and the training of staff on data-protection policies. However, the study is limited by its exclusion of non-English-language studies and the need for additional resources to deepen the findings. To overcome these limitations, future research could expand existing knowledge and collect more comprehensive data to better understand the complex issues examined. Full article
(This article belongs to the Special Issue Literature Reviews in Finance)
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11 pages, 269 KiB  
Article
The Retained Earnings Effect on the Firm’s Market Value: Evidence from Jordan
by Firas N. Dahmash, Hashem Alshurafat, Raed Hendawi, Abdallah Bader Alzoubi and Hamzeh Al Amosh
Int. J. Financial Stud. 2023, 11(3), 89; https://doi.org/10.3390/ijfs11030089 - 04 Jul 2023
Cited by 1 | Viewed by 4264
Abstract
The aim of this study was to investigate the effect of the retention per share compared to the dividend per share by modeling the firm’s market value as a function of the retention per share and the dividend per share for all firms [...] Read more.
The aim of this study was to investigate the effect of the retention per share compared to the dividend per share by modeling the firm’s market value as a function of the retention per share and the dividend per share for all firms in the Jordanian context using unbalanced panel data analysis for a sample of 2281 firm years covering the period from 2010 to 2021. The results of the pooled sample indicated a strong positive significant effect for dividends per share. However, the retention per share indicated a negative significant effect on the firm’s market value. The other robustness analysis for the two sub-samples and the financial and non-financial sub-samples indicated the same results, consistent with the pooled sample for the two main explanatory variables. Full article
22 pages, 2637 KiB  
Article
Modeling Supply Chain Firms’ Stock Prices in the Fertilizer Industry through Innovative Cryptocurrency Market Big Data
by Damianos P. Sakas, Nikolaos T. Giannakopoulos, Markos Margaritis and Nikos Kanellos
Int. J. Financial Stud. 2023, 11(3), 88; https://doi.org/10.3390/ijfs11030088 - 03 Jul 2023
Cited by 1 | Viewed by 1759
Abstract
Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. [...] Read more.
Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets’ user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model’s simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin’s website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites’ organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry’s stock prices, while Bitcoin’s website organic keywords variation decreases accordingly. Full article
(This article belongs to the Special Issue Digital and Conventional Assets)
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15 pages, 314 KiB  
Article
Can Digital Financial Inclusion Promote Women’s Labor Force Participation? Microlevel Evidence from Africa
by Imane Elouardighi and Kenza Oubejja
Int. J. Financial Stud. 2023, 11(3), 87; https://doi.org/10.3390/ijfs11030087 - 03 Jul 2023
Cited by 2 | Viewed by 2009
Abstract
Our study analyzes the relationship between digital financial inclusion and women’s labor force participation, as well as shedding light on the barriers to women’s digital financial inclusion. We have mobilized a microeconomic database that covers 15,192 African women. Our database is extracted from [...] Read more.
Our study analyzes the relationship between digital financial inclusion and women’s labor force participation, as well as shedding light on the barriers to women’s digital financial inclusion. We have mobilized a microeconomic database that covers 15,192 African women. Our database is extracted from the Global Findex database, 2021 edition, based on nationally representative surveys of 29 African countries. The Probit model estimation methodology is used to examine the empirical results. Our findings reveal that financial inclusion via the digital channel is positively associated with women’s labor force participation more than the traditional channel. A significant and positive impact of formal financial services channels on the level of women’s participation in the labor market was uncovered. Our research has shown that women face a variety of obstacles when it comes to accessing financial services, both through traditional channels and digital means. These barriers include nonvoluntary obstacles in traditional financial inclusion channels. However, as a woman’s income level increases, the intensity of these barriers decreases. When it comes to digital financial inclusion, women often face a unique set of obstacles, such as the high cost of mobile financial services, lack of money, and lack of access to a cellphone. The study contributes to the existing literature by investigating the impact of digital financial inclusion on women’s labor force participation in African countries and identifying barriers that hinder women’s digital financial inclusion based on individual-level data. It suggests that African policymakers should increase women’s financial inclusion through digital channels to improve their participation in the labor market. Full article
(This article belongs to the Special Issue Digital Financial Inclusion)
15 pages, 750 KiB  
Article
The Role of Social Banking in the Success and Sustainable Business Continuity of SSMEs
by Eirini Stavropoulou, Konstantinos Spinthiropoulos, Alexandros Garefalakis, Konstantina Ragazou and Fragkiskos Gonidakis
Int. J. Financial Stud. 2023, 11(3), 86; https://doi.org/10.3390/ijfs11030086 - 28 Jun 2023
Cited by 1 | Viewed by 3583
Abstract
The technological developments in the social economy have significant implications for social banks and are optimistically changing the way social retail banks conduct their business. Social banks can invest in social services for small- and medium-sized enterprises (SSMEs) either to acquire a strategic [...] Read more.
The technological developments in the social economy have significant implications for social banks and are optimistically changing the way social retail banks conduct their business. Social banks can invest in social services for small- and medium-sized enterprises (SSMEs) either to acquire a strategic advantage or out of strategic necessity. With the assistance of a mathematical model, this study tries to identify SME service channels and assess potential impacts on social deposit banks’ performance. In the first stage, the proposed model estimates the predictive capacity of interpretive accounting variables (financial ratios) versus the interpreted accounting variable (future quarterly earnings before taxes (EBT)). Then, in the second stage, the SSME service channels were added to the earnings before tax model in terms of profitability measure, which informs corporate earnings before operating the business to account for the income tax attributed to it for the purpose of estimating their impact on the performance of social banks. According to our findings, the banks are investing in SME services just to validate their investments in SME services as a strategic necessity. SSMEs services do not provide any strategic advantage to any banks in terms of financial or accounting performance or efficiency since the banks are already efficient. Investing in SMEs is a tool for preserving their strategic positions. Therefore, the contribution of this study is focused on the fact that it highlights the impact of financing the social deposit banking industry on institutions, while most studies analyze the vice versa interaction. Full article
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